A number of techniques are known for adjusting the contrast of an image, particularly in order to improve the contrast and therefore visibility of the image and to increase depth perception. Contrast adjustment or enhancement of images, particularly digital images, is used in many fields, including enhancing the contrast of a digital image for display by a television receiver or other display device, for printing by a printer, in digital cameras, etc., etc. Contrast enhancement is used to improve the contrast in medical and other images.
Global contrast enhancement techniques remedy problems that manifest themselves in a global fashion, such as excessive or poor lighting conditions in the source environment. On the other hand, local contrast enhancement attempts to enhance the visibility of local details in the image.
A particular known local contrast enhancement method uses “unsharp masking”, which is shown schematically in FIG. 1. The original image 1 is input to a smoothing filter 2, which “smoothes” the original image to remove the high frequency component so that the output of the smoothing filter 2 is the low frequency component of the original image. Then, the low frequency component of the original image is subtracted from the original image 1 in a summer 3, the output of which is therefore the high frequency component of the image. That high frequency component is then amplified by a fixed gain in a multiplier 4. The amplified high frequency component is then added back to the original image in a further summer 5, the output of which is therefore the enhanced image 6. However, this particular method amplifies the high frequency component with a fixed gain factor. This causes ringing or overshoot effects around the edges or other regions of high contrast in the original image because of the high values of the high frequency component around the edges after the amplification. In addition, this method causes noise in smooth areas of the image, which manifests itself by a grainy look in what should be smooth areas in the enhanced image. An example of this type of unsharp masking is disclosed in “Digital Image Enhancement and Noise Filtering by Using Local Statistics”, IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-2, pp. 165-168, February 1980, by J. S. Lee.
Modified versions of unsharp masking are known in which the gain applied to the high frequency component of the original image by the multiplier 4 is made to vary according to certain conditions. An example is disclosed in “Real-time Adaptive Contrast Enhancement”, IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-3, pp. 655-661, June 1981, by P. M. Narendra and R. C. Fitch. In the method of that paper, the gain that is used to amplify the high frequency component is made to be inversely proportional to the local variance in the high frequency component. This modified method therefore adapts the spatial gain applied to the high frequency component according to local statistics in the high frequency component. However, this causes the gain to become very large when the local variance is small, which leads to noise amplification in smooth (low contrast) areas of the input image 1. A further modification of the adaptive contrast enhancement techniques is disclosed in “Image Enhancement via Adaptive Unsharp Masking”, IEEE Trans. On Image Processing, vol. 9, no. 3, March 2000, by A. Polesel, G. Ramponi and V. J. Matthews. In this modified technique, an adaptive filter is used to emphasise the medium contrast details in the image more than large contrast regions such as edges. However, the filter that is disclosed in this paper is a Laplacian filter which therefore has three tap coefficients and which is therefore computationally complex. As disclosed, the method of this paper requires 17 multiplications and one division operation to compute the output brightness level data for each pixel.
In our U.S. patent application Ser. No. 11/340,956 of 26 Jan. 2006 and related patent applications, the entire content of which is hereby incorporated by reference for all purposes, we disclose a further example of an adaptive contrast enhancement technique in which the filtering is carried out by at least one recursive infinite impulse response filter having a single delay coefficient that is adaptive to areas of high contrast in the input image. Whilst this method is computationally relatively efficient, especially when compared for example to the method disclosed by Polesel, Ramponi and Matthews, the method nevertheless requires a relatively large number of calculations to be carried out and a number of distinct steps. This makes it difficult to implement the method at low cost, which is of course of particular importance in consumer equipment, including for example television receivers or other domestic equipment having a display device.